AI Risk Management: A CISO's Framework for 2026

Why AI Risk Management Demands a New Approach

Traditional cybersecurity frameworks weren't designed for AI systems. In penetration tests I've conducted, organizations often apply the same controls to their ML pipelines that they use for conventional applications—only to discover that attackers exploit entirely different attack surfaces.

AI introduces unique risks that standard security frameworks miss:

• Model vulnerabilities that don't exist in traditional software
• Data poisoning attacks that corrupt training pipelines
• Prompt injection in LLM applications (see our complete guide to prompt injection)
• Adversarial inputs that bypass ML classifiers
• Model extraction and intellectual property theft

This framework addresses these gaps, providing CISOs with a structured approach to AI risk that integrates with existing security programs.

The AI Risk Landscape in 2026

Before diving into the framework, it's essential to understand what's changed. In our analysis of adversarial attacks from 2025, we documented a significant shift:

• Attacks on AI systems increased 300% compared to 2024
• Supply chain attacks targeting training data became mainstream
• Regulatory penalties for AI-related incidents began materializing under the EU AI Act
• Model theft emerged as a top intellectual property concern

The Three Pillars of AI Risk

AI risk divides into three interconnected categories:

1. Model Risk

Vulnerabilities inherent to the machine learning model itself:

• Adversarial robustness failures
• Model drift and degradation
• Bias and fairness issues
• Lack of explainability

2. Data Risk

Threats to the data used for training and inference:

• Training data poisoning
• Privacy leakage through model outputs
• Membership inference attacks
• Data lineage and provenance failures

3. Infrastructure Risk

Security of the systems running AI workloads:

• Model serving API vulnerabilities
• Container and orchestration weaknesses
• Supply chain risks in ML libraries
• Access control for model artifacts

The CISO's AI Risk Framework

Phase 1: AI Asset Discovery and Classification

You can't secure what you don't know about. In assessments, I frequently find "shadow AI" deployed by business units without security involvement.

Key Actions:

• Inventory all AI systems: Document models, training data sources, inference endpoints, and downstream consumers
• Classify by risk tier: Use a framework similar to the EU AI Act's risk categories (prohibited, high-risk, limited risk, minimal risk)
• Map data flows: Understand where training data originates and where predictions flow
• Identify third-party dependencies: Document all ML frameworks, pre-trained models, and external APIs

From a recent assessment: A financial services client discovered 47 AI models in production—only 12 were known to the security team. The rest had been deployed by data science teams using cloud resources provisioned outside IT governance.

Phase 2: Risk Assessment Methodology

Adapt your existing risk assessment process for AI-specific factors.

Assessment Criteria:

Factor	Questions to Ask
Business Impact	What decisions does the AI inform? What happens if it fails or is manipulated?
Data Sensitivity	What data was used for training? Can the model leak sensitive information?
Exposure Surface	Who can query the model? Are inputs user-controlled or from trusted sources?
Regulatory Context	Does this fall under AI-specific regulations (EU AI Act, sector-specific requirements)?
Adversarial Exposure	Would attackers benefit from manipulating outputs? Is the model publicly accessible?

Phase 3: Control Implementation

Implement controls across the AI lifecycle:

Development Controls

• Secure ML pipeline: Implement access controls and audit logging for training jobs
• Data validation: Verify training data integrity and provenance
• Model testing: Include adversarial robustness testing in your ML workflow (see our AI red teaming methodology)
• Documentation: Maintain model cards and data sheets for governance

Deployment Controls

• Input validation: Sanitize and validate all inputs to model inference endpoints
• Rate limiting: Prevent model extraction through excessive querying
• Output filtering: Monitor for sensitive data leakage in model outputs
• Access control: Implement proper authentication and authorization for model APIs

Operational Controls

• Monitoring: Track model performance and drift; alert on anomalies
• Incident response: Include AI-specific scenarios in your IR playbook
• Regular testing: Conduct periodic penetration testing of AI systems
• Update management: Process for patching ML dependencies and retraining models

Phase 4: Compliance Alignment

Map your controls to relevant frameworks and regulations:

Key Frameworks:

• NIST AI RMF: The AI Risk Management Framework provides comprehensive guidance on governance, mapping, measurement, and management
• EU AI Act: If operating in Europe, classify systems and implement required controls for high-risk AI
• ISO/IEC 42001: The AI management system standard for organizational governance
• OWASP ML Top 10: Technical controls for the most critical ML security risks

For a deeper dive into regulatory requirements, see our guide on AI Governance & Compliance.

Building Your AI Security Team

AI security requires skills that most security teams don't have. Consider:

Skill Gaps to Address:

• ML engineering knowledge: Understanding how models are built and deployed
• Adversarial ML expertise: Knowledge of attack techniques and defenses
• Data science literacy: Ability to assess data quality and bias
• AI testing skills: Penetration testing for ML systems

Staffing Options:

• Upskill existing security engineers in AI/ML security
• Partner with data science teams (with clear security ownership)
• Engage external specialists for assessments and specialized testing

Measuring AI Risk Program Maturity

Use this maturity model to assess your current state:

Level	Characteristics
Level 1: Ad Hoc	No formal AI security; reactive responses to incidents
Level 2: Developing	AI inventory exists; basic controls for high-risk systems; security reviews for new deployments
Level 3: Defined	Formal AI risk framework; standardized assessments; regular testing; compliance mapping
Level 4: Managed	Continuous monitoring; metrics-driven improvements; proactive threat intelligence; mature incident response
Level 5: Optimized	Security integrated into ML pipeline; automated testing; industry leadership; contributes to standards

Quick Wins: 30-Day Action Plan

For organizations just starting their AI risk journey:

Week 1: Discovery

• Audit all AI systems in production and development
• Identify data owners and business criticality
• Document any existing security controls

Week 2: Prioritization

• Risk-rank discovered systems
• Identify high-exposure, high-impact systems for immediate attention
• Flag systems falling under regulatory requirements

Week 3: Initial Controls

• Implement access controls for top-priority systems
• Add monitoring to model inference endpoints
• Review API security for ML endpoints (use our API Security Testing Checklist)

Week 4: Program Foundation

• Draft AI security policy
• Define roles and responsibilities
• Schedule first formal AI security assessment

Conclusion

AI risk management isn't about building a separate security program—it's about extending your existing capabilities to address AI-specific threats. Start with asset discovery, implement risk-based controls, and build toward a mature, integrated program.

The organizations that succeed are those that treat AI security as an ongoing discipline, not a one-time project. Regular testing, continuous monitoring, and adaptation to evolving threats are essential.